@conference {237116,
	title = {Accelerated Sampling for the Indian Buffet Process},
	booktitle = {Proceedings of the 26th International Conference on Machine Learning},
	year = {2009},
	address = {Montreal, Canada},
	abstract = {We often seek to identify co-occurring hidden features in a set of observations. The Indian Buffet Process (IBP) provides a nonparametric prior on the features present in each observation, but current inference techniques for the IBP often scale poorly. The collapsed Gibbs sampler for the IBP has a running time cubic in the number of observations, and the uncollapsed Gibbs sampler, while linear, is often slow to mix. We present a new linear-time collapsed Gibbs sampler for conjugate likelihood models and demonstrate its efficacy on large real-world datasets.},
	author = {Finale Doshi-Velez and Zoubin Ghahramani}
}
